Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data
Foody, G.M. and Cutler, M.E. (2002) Remote sensing of biodiversity: Using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat TM data. In, Papers in proceedings of the IGARSS '02 conference. Geoscience and Remote Sensing Symposium, IGARSS 2002 IEEE International Piscataway, N.J., USA, IEEE, 497-499. (doi: 10.1109/IGARSS.2002.1025085).
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Two types of neural network were used to derive measures of biodiversity from Landsat TM data of a tropical rainforest. A feedforward neural network was used to estimate species richness while a Kohonen neural network was used to provide information on species composition. The results indicate the potential of remote sensing as a source of maps of biodiversity.
|Item Type:||Book Section|
|Subjects:||G Geography. Anthropology. Recreation > G Geography (General)|
|Divisions:||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Date Deposited:||30 Mar 2005|
|Last Modified:||02 Mar 2012 11:44|
|Contributors:||Foody, G.M. (Author)
Cutler, M.E. (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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